标题： 基于神经数据变压器的神经种群活动表示学习 显示英文标题

标题： Representation learning for neural population activity with Neural Data Transformers

摘要： 神经种群活动被理论认为反映了潜在的动力学结构。 这种结构可以使用具有显式动态的状态空间模型准确捕捉，例如基于循环神经网络（RNNs）的模型。 然而，使用循环结构来显式建模动态需要数据的顺序处理，这会减慢实时应用，如脑机接口。 在这里，我们介绍了神经数据变换器（NDT），一种非循环的替代方法。 我们通过将NDT应用于具有已知动态的合成数据集以及在抓取任务中来自猴子运动皮层的数据来测试其捕捉自主动力学系统的能力，这些数据由RNNs很好地建模。 NDT对这些数据集的建模效果与最先进的循环模型相当。 此外，其非循环性使得推理时间为3.9毫秒，完全在实时应用的循环时间范围内，并且比猴子抓取数据集上的循环基线快了6倍以上。 这些结果表明，不需要显式动力学模型来建模自主的神经种群动态。 代码：https://github.com/snel-repo/neural-data-transformers 显示英文摘要

摘要： Neural population activity is theorized to reflect an underlying dynamical structure. This structure can be accurately captured using state space models with explicit dynamics, such as those based on recurrent neural networks (RNNs). However, using recurrence to explicitly model dynamics necessitates sequential processing of data, slowing real-time applications such as brain-computer interfaces. Here we introduce the Neural Data Transformer (NDT), a non-recurrent alternative. We test the NDT's ability to capture autonomous dynamical systems by applying it to synthetic datasets with known dynamics and data from monkey motor cortex during a reaching task well-modeled by RNNs. The NDT models these datasets as well as state-of-the-art recurrent models. Further, its non-recurrence enables 3.9ms inference, well within the loop time of real-time applications and more than 6 times faster than recurrent baselines on the monkey reaching dataset. These results suggest that an explicit dynamics model is not necessary to model autonomous neural population dynamics. Code: https://github.com/snel-repo/neural-data-transformers